Neural networks as 'software sensors' in enzyme production

Industrial applications of enzyme technology are rapidly increasing. On-line control of enzyme production processes, however, is difficult, owing to the uncertainties typical of biological reactions and to the lack of suitable sensors. We demonstrate that well-trained feedforward backpropagation neural networks with one hidden layer can be employed to overcome such problems with no need for a priori knowledge of the relationships of the process variables involved. Neural network programs were written in Microsoft Visual C+2 for Windows and implemented in a personal computer. The goodness of fit of the trained neural network to the reference data was determined by the coefficient of determination R2. On-line state estimation and multi-step ahead prediction of enzyme activity and biomass concentration, both in a yeast lipase and fungal glucoamylase production could be satisfactorily carried out. Results showed an excellent fit for estimated lipase activity (R2 = 0.988) and biomass concentration (R2 = 0.989). In glucoamylase production, both enzyme activity and biomass concentration could also be reliably predicted for 2 time intervals (10 h) ahead with only on-line measurable parameter values as the input data.